The present invention relates to a real-time, high-frequency, high-resolution cerebral biopotential analysis system and method, and more particularly to a computer-based biopotential diagnostic system and method for quantitatively determining, in a noninvasive manner, cerebral phenomena that can be ascertained by analyzing cerebral electrical activity.
Despite a considerable expenditure of time and effort, current approaches to the quantitative, noninvasive assessment of cerebral electrical activity, as displayed in an "EEG" waveform, have not been successful in fully extracting all of the information which is present in this complex waveform. A great need remains for an accurate, sensitive, reliable, and practical neurologic profiling technology. In particular, contemporary intra-operative EEG monitoring techniques have not been widely adopted due to their inherent limitations. Indeed eighty percent (80%) of all medical malpractice suits are believed to be related to post-anesthesia morbidity and mortality, and if such EEG monitoring techniques were reliable they certainly would have been adopted.
A number of devices known in the prior art are capable of tracking cerebral activity qualitatively. Techniques involving the use of the "classical", conventional analog EEG are restricted to analyses in the time domain, and require considerable training for adequate interpretation. Moreover, since the frequency resolution of the human eye at standard speeds and gain is 30-60 Hz, much high frequency content is invisible. Thus visual EEG assessment is better characterized as being an art rather than a science. In fact, it has been shown that the average correlation between seven experienced readers did not exceed 56 per cent.
The use of frequency (power spectrum) analysis of the EEG in the 1960's introduced the notion of some basic processing of the signal prior to visual inspection and led to the application of frequency analysis of the EEG to various cerebral monitoring problems. In the past 25 years at least 100 papers have been published in the medical literature describing applications of power spectral analysis for purposes such as assessing the depth of anesthesia and cerebral ischemia under various intraoperative conditions. U.S. Pat. No. 4,557,270 issued to John also describes the use of power spectral analysis to evaluate cerebral perfusion during open heart surgery. Several recent studies, however, have shown many deficiencies in the use of power spectral analysis to monitor cerebral perfusion and to determine post operative neurologic outcome. In addition, neither power spectral analysis nor any other monitoring technique has been shown to be reliable, and this is demonstrated by the fact that the well-accepted Harvard Medical School Anesthesia Monitoring Standard does not include any type of intraoperative neurologic monitoring due, in all likelihood, to the complexity of interpreting raw EEG data and the unreliability of existing automated systems utilizing power spectral or time-domain analytic techniques.
The discharge of thousands of bio-electrically active cells in the brain, organized in larger, interacting neural centers contributes to the formation of an electrical signal with a wide frequency spectrum and extremely complex dynamics. Embedded in that signal is information regarding frequency content, non-linearities, and phase relationships arising from the complex neuronal firing patterns that take place. Because of the complexity of the EEG signal, conventional time and frequency modes of analysis have not been adequate t fully profile its behavior. In the Fourier transform of the second order autocorrelation function (the power spectrum) processes are represented as a linear summation of statistically uncorrelated sine-shaped wave components. Contemporary approaches to monitoring the EEG by means of the power spectrum have thus suppressed information regarding non-linearities and inter-frequency phase relationships and are of limited utility in representing the EEG's dynamic structure. Furthermore the high frequency low amplitude elements of the EEG have been discarded to date by the filtering and sampling characteristics of known analysis techniques.
Because the EEG has a wide spectrum and is highly dynamic and non-linear, the phase relationships within the EEG, especially in the higher frequencies, must carry a great deal of diagnostic information regarding cerebral function. The Fourier transform of the third order autocorrelation function, or autobispectrum, is an analytic process that quantifies deviation from normality, quadratic non-linearities and inter-frequency phase relationships within a signal. The Fourier transform of the third order crosscorrelation function, or crossbispectrum, is an analytic process that provides similar information between two signals.
Autobispectral analytic techniques have been applied to the EEG signal and the basic bispectral properties of the conventional EEG focusing o frequencies below 32 Hz have been investigated. Such studies have also been conducted to search for changes between waking and sleeping by means of autobispectral analysis. Autobispectral analysis and power spectral analysis have also been used in an attempt to show that the EEGs of monozygotic twins are similar in structure.
To date, no previous study has examined the high frequency (greater than 32 Hz) content of the EEG and found information of diagnostic value. It also does not appear that any study has shown autobispectral or crossbispectral analysis to be of any value for any diagnostic purpose and certainly neither of these analytic techniques have been shown to have any value in quantifying depth and adequacy of anesthesia, pain responses induced by surgical stress, cerebral ischemia, consciousness, degrees of intoxication, ongoing cognitive processes or interhemispheric dynamic phase relations.
It is therefore a principal object of the present invention to provide a noninvasive high resolution high frequency electroencephalographic system and method capable of recognizing and monitoring physical phenomena that are reflected in cerebral electrical activity.
Another object of the present invention is to provide a noninvasive electroencephalographic system and method capable of determining and monitoring depth and adequacy of anesthesia, pain responses during surgical stress, acute cerebral ischemia, level of consciousness, degrees of intoxication and normal or abnormal cognitive processes.
Accordingly, the system and method of the present invention utilizes a suitable electrode and amplifier system to obtain 19 unipolar EEG signals from regions of interest on both left and right hemispheres of a subject's brain. Band-pass filtering of 2-500 Hz is used to obtain signals with a high frequency content. High gain amplifiers maximize the dynamic range for the high frequency, low energy wave components of the signals. The system applies digital sampling techniques to the signals and transmits digitized data over a high speed serial line to a host computer. The system divides a 32 second long data segment from each lead into 128 consecutive 0.25 second intervals. The system normalizes all 19 unipolar leads by the standard deviation, and then characterizes the dynamic phase relations within the signal by processing for autobispectral variables using either a Fast Fourier Transform (FFT) based approach, or a parametric cubic fitting approach. Similarly three corresponding left and right hemisphere data pairs are normalized in the same manner and dynamic phase relations between two hemispheres are then characterized by processing for crossbispectral estimates utilizing either the FFT or parametric based techniques. The outcome is a set of two dimensional arrays representing the dynamic interactions between all the possible combinations of frequencies (frequency pairs) in the spectrum of interest. For each unipolar lead, three arrays are produced: autobicoherence, autobispectral density and autobiphase. Three arrays are also generated for each bipolar data set: crossbicoherence, crossbispectral density and crossbiphase.
Each of the autobispectral and crossbispectral arrays contains 16,5I2 data points. Although all, or nearly all, of these values can be expected to change from normal during different interventions or due to differing disease states, in the preferred embodiment only those points which show the greatest fidelity in tracking the particular diagnostic determination in question are utilized to create a diagnostic criterion. The ensemble of points most sensitive to a particular intervention or ongoing physiologic process can be used to create a clinically useful single-number index from the computed bispectral arrays. The system uses these indices as a diagnostic figure of merit for the assessment of depth and adequacy of anesthesia, pain responses during surgical stress, acute cerebral ischemia, level of consciousness, degree of intoxication and normal or abnormal cognitive processes. This approach makes it possible for any, even unskilled, operator to meaningfully interpret the output of the diagnostic device.
In situations where continuous monitoring is required, indices can be continuously displayed on a video terminal thereby enabling the operator to interactively evaluate regions of interest. For record keeping purposes index values and other pertinent variables can be sent to a hard copy output device or stored on a disk.
These and other objects and features of the present invention will be more fully understood from the following detailed description which should be read in light of the accompanying drawings in which corresponding reference numerals refer to corresponding parts throughout the several views.